Abstract Background After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. Methods We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. Results Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13–4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71–3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48–3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72–8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities ( I 2 &gt; 80%; I 2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics). Conclusions Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves. 

pdf/assessing-changes-in-incubation-period-serial-interval-and-4ikij46v4b.pdf

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

Background: After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. Methods: We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. Results: Our study included 274 records obtained from 147 household studies, contact tracing studies or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods. Specifically, we found that Omicron had the shortest pooled estimates for the incubation period (3.63 days, 95%CI: 3.25-4.02 days), serial interval (3.19 days, 95%CI: 2.95-3.43 days), and realized generation time (2.96 days, 95%CI: 2.54-3.38 days) whereas the ancestral lineage had the highest pooled estimates for each of them. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. We found considerable heterogeneities (I2 > 80%) when pooling the estimates across different virus lineages, indicating potential unmeasured confounding from population factors (e.g., social behavior, deployed interventions). Conclusion: Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves. Keywords: COVID-19, variants of concern, incubation period, serial interval, realized generation time, intrinsic generation time, systematic review, meta-analysis

/pdf/assessing-changes-in-incubation-period-serial-interval-and-1tokysx0.pdf

In communication signal recognition, there are problems such as a tedious feature extraction process and low applicability of extracted features. This paper simulates wireless communication channels and suggests an algorithm that uses nearest neighbor component analysis (NCA) along with convolutional neural networks (CNN) for classification. The algorithm chooses wavelet entropy (WE), wavelet approximate energy ratio (WAER), and the first 2–4 singular values as the core features. Eight different forms of modulations, including GFSK, CPFSK, B-FM, DSB-AM, SSB-AM, BPSK, QPSK and PAM4 would be automatically classified using the technique. According to the experiment results, the average recognition accuracy for the eight signals is 93.6% when the signal-to-noise ratio is 30dB. In addition, this paper also discusses the results and accuracy of the model to identify 6 and 10 types of signal modulation and studies the accuracy of the recognition under different signal-to-noise ratios, verifying the robustness of the model. 

Automatic modulation recognition of communication signal based on wavelet transform combined with singular value and NCA-CNN

In communication signal recognition, there are problems such as a tedious feature extraction process and low applicability of extracted features. This paper simulates wireless communication channels and suggests an algorithm that uses nearest neighbor component analysis (NCA) along with convolutional neural networks (CNN) for classification. The algorithm chooses wavelet entropy (WE), wavelet approximate energy ratio (WAER), and the first 2–4 singular values as the core features. Eight different forms of modulations, including GFSK, CPFSK, B-FM, DSB-AM, SSB-AM, BPSK, QPSK and PAM4 would be automatically classified using the technique. According to the experiment results, the average recognition accuracy for the eight signals is 93.6% when the signal-to-noise ratio is 30dB. In addition, this paper also discusses the results and accuracy of the model to identify 6 and 10 types of signal modulation and studies the accuracy of the recognition under different signal-to-noise ratios, verifying the robustness of the model.

Abstract To estimate the reproductive numbers (R t ) , it is essential to infer generation time, which is often approximated by serial interval (SI) . The SI based on clinical outcomes is not free from recall biases and even such clinical information is not always available. We defined diagnostic serial interval (SI d ) , as a potential alternative metric and compared with traditional SI . We analyzed confirmed COVID-19 cases from three ancestral waves in Hong Kong and the first wave in mainland China. Using Bayesian methods, we inferred the distributions of effective SI and SI d , along with onset-to-reporting delays, and compared the resulting estimates. The distributions of SI and SI d were comparable across waves, with shorter means observed in SI d . Reporting delays for infectors ( d 1 ) were longer than these for infectees (d 2 ), which was identified as a key factor influencing the temporal variation in SI d . The PHSMs, case profile and demography were also found to be significant factors of SI d . Time-varying R t estimates derived from both SI and SI d were comparable, with median absolute differences ranging from 0.12 to 0.19. Therefore, SI d shows potentials as an alternative metric to SI for estimating R t in assessing COVID-19 transmission dynamics can be extended for other respiratory viruses. 

pdf/diagnostic-serial-interval-as-an-alternative-measure-of-g1tlfs0dmcau.pdf

Diagnostic serial interval as an alternative measure of clinical serial interval using ancestral COVID-19 waves in Hong Kong and mainland China

The serial interval distribution is used to approximate the generation time distribution, an essential parameter to predict the effective reproductive number "R_t", a measure of transmissibility. However, serial interval distributions may change as an epidemic progresses rather than remaining constant. Here we show that serial intervals in Hong Kong varied over time, closely associated with the temporal variation in COVID-19 case profiles and public health and social measures that were implemented in response to surges in community transmission. Quantification of the variation over time in serial intervals led to improved estimation of R_t, and provided additional insights into the impact of public health measures on transmission of infections.

/pdf/insights-into-covid-19-epidemiology-and-control-from-27uu3n0l.pdf

Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong

. This paper presents a new algorithm of the singular value decomposition (SVD) in the wavelet domain for ground penetrating radar (GPR) to remove direct coupled waves. In fact, direct coupled waves commonly disturb the reﬂecting waves from underground targets. Besides, the amplitude and energy of direct coupled waves are large, which reduces the resolution of the images to the targets and ad-versely aﬀects the subsequent image interpretation work. The GPR signal is decomposed into several levels by Wavelet to obtain approximation components and detailed components of each level. The information of targets is contained in big eigenvalues of detail components, while the direct coupled waves are contained in small ones. Therefore, the SVD in the wavelet domain can reduce the misjudgment of eﬀective signals and improve the signal to noise ratio (SNR) of GPR signals. The simulated and ﬁeld GPR data show that the SVD in the wavelet domain denoising method has better results for direct coupled wave removal than the traditional methods, which validates the eﬀectiveness of the proposed denoising method.

/pdf/direct-coupled-wave-removal-for-gpr-data-based-on-svd-in-the-18db04p8.pdf

Direct Coupled Wave Removal for GPR Data Based on SVD in the Wavelet Domain

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Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong

Direct Coupled Wave Removal for GPR Data Based on SVD in the Wavelet Domain